A 30M-token allowance sounds enormous until you divide it by your observed output length. At 200 tokens per generated response the ceiling is roughly 150,000 requests; at 800 tokens it is about 37,500. On a free server, the more useful number is tail latency under queuing, because a generous quota does not prevent connection stalls, 429s, or truncated streams. MonkeyCode advertises free model access, a free server tier, and an open-source project path that removes per-call cost while you measure this. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Start with the capacity you will actually consume
| Output tokens per request | Requests before a 30M-token ceiling |
|---|---|
| 100 | 300,000 |
| 200 | 150,000 |
| 500 | 60,000 |
| 800 | 37,500 |
| 1,200 | 25,000 |
Token ceilings are a procurement number. Your route has a different request ceiling once chunking, RAG context, function-call schemas, and follow-ups are included. Record the average prompt and completion tokens from 50 representative calls in your integration, not the vendor's sample. That average determines whether you are testing a route that can absorb 25,000 user turns or 300,000 user turns.
The test harness
Point this at any OpenAI-compatible streaming endpoint. It issues REQUESTS calls with CONCURRENCY workers and records first-token time, total duration, status, and streamed chunk count. This is the artifact; run it before and after changing route code.
import os, json, statistics, time
from concurrent.futures import ThreadPoolExecutor
import httpx
BASE_URL = os.environ['MODEL_BASE_URL'].rstrip('/')
API_KEY = os.environ['MODEL_API_KEY']
MODEL = os.environ['MODEL_NAME']
CONCURRENCY = int(os.environ.get('CONCURRENCY', '8'))
REQUESTS = int(os.environ.get('REQUESTS', '40'))
def one_call(i):
started = time.perf_counter()
first = None
chunks = 0
usage = None
with httpx.stream(
'POST',
f'{BASE_URL}/chat/completions',
headers={'Authorization': f'Bearer {API_KEY}'},
json={
'model': MODEL,
'messages': [{'role': 'user', 'content': 'List five ways to reduce duplicate API calls.'}],
'max_tokens': 128,
'stream': True,
},
timeout=60,
) as r:
status = r.status_code
if status >= 400:
return {'status': status, 'ttft_ms': None, 'duration_ms': (time.perf_counter() - started) * 1000, 'chunks': 0, 'usage': r.text[:200]}
for line in r.iter_lines():
if not line.startswith('data:'):
continue
payload = line[5:].strip()
if payload == '[DONE]':
break
try:
event = json.loads(payload)
except json.JSONDecodeError:
continue
if first is None:
first = time.perf_counter()
chunks += 1
if event.get('usage'):
usage = event['usage']
return {
'status': status,
'ttft_ms': (first - started) * 1000 if first else None,
'duration_ms': (time.perf_counter() - started) * 1000,
'chunks': chunks,
'usage': usage,
}
with ThreadPoolExecutor(max_workers=CONCURRENCY) as pool:
results = list(pool.map(one_call, range(REQUESTS)))
errors = [r for r in results if r['status'] >= 400]
ok = [r for r in results if r['status'] < 400]
ttfts = sorted(r['ttft_ms'] for r in ok if r['ttft_ms'] is not None)
durations = sorted(r['duration_ms'] for r in ok)
def pct(xs, p):
if not xs:
return None
k = min(len(xs) - 1, int(len(xs) * p))
return round(xs[k], 1)
print(json.dumps({
'requests': len(results),
'error_rate': round(len(errors) / len(results), 4),
'p50_ttft_ms': pct(ttfts, 0.50),
'p95_ttft_ms': pct(ttfts, 0.95),
'p95_duration_ms': pct(durations, 0.95),
'chunks_per_request': round(statistics.mean(r['chunks'] for r in ok), 1) if ok else 0,
}, indent=2))
A streamed chunk is not necessarily a token; treat chunk count as a proxy for progress and prefer the provider's final usage object when present.
Read the two numbers that actually block a rollout
| Signal | Measured value | Example fail threshold | Why it matters |
|---|---|---|---|
| Rejections | HTTP 429/5xx error rate | >2% | Shared free infrastructure often queues requests as rate limits |
| Time to first token | p95 | >4 seconds | Users abandon before the token ceiling matters |
| Total duration | p95 | >15 seconds | Long streams hold worker slots and amplify retries |
| Truncation | chunks per request | stopping before [DONE] | Proxy or streaming bugs can silently end output |
These thresholds are starting controls, not vendor guarantees. Keep the actual numbers in the PR alongside a date, region, concurrency, and model identifier so later failures are attributable to a change instead of a vibes-based regression.
Decision table for the read-only slice
| Observed behavior | Action |
|---|---|
| p95 TTFT <=4s and error rate <=2% | Keep the route as a read-only shadow and begin adding persistence |
| p95 TTFT 4-8s or error rate 2-5% | Add client-side timeout and one idempotent retry, but do not grant write authority |
| p95 TTFT >8s or error rate >5% | Reduce concurrency to 2; if it does not recover, use the free tier only as a load-test substrate |
| Any 5xx on a write path without an idempotency key | Stop and fix idempotency before merging |
What this does not prove
The harness produces a point-in-time tail-latency report. It does not prove correctness, data safety, capacity guarantees, or that a free server will behave the same way next week. Free capacity can be rebalanced, rate limits can shift, and queue depth can change with tenant load. Pair this artifact with a golden-set evaluation for quality and a permission check before any write. Do not use a free server for regulated data, stable customer-facing traffic, or any payload where a truncated answer can cause harm. If you need a contractual SLO or isolated throughput, a paid endpoint is the correct boundary; the free tier is for validation and controlled slices.
Who should skip this: teams that need a hard SLA, production writes without idempotency, or PII/regulated text. The harness will not make a shared free tier production-safe.
If you want to avoid merging a route on quota vibes, point the harness at MonkeyCode's free endpoint first and keep the five-number report next to the PR.
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